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Kwajalein show varying degrees of agreement. The surface rain rates are then classified into convective and stratiform rain
types over ocean, land, and coastal areas for more detailed comparisons to the ground radar measurements. These comparisons
lead to a better understanding of the relative performances of the current TRMM rain algorithms. For example, at Melbourne
more than 80\% of the radar-derived rainfall is classified as convective rain. Convective rain from the TRMM rain algorithms
is less than that from ground radar measurements, while TRMM stratiform rain is much greater. Rain area coverage from
2A-12 is also in reasonable agreement with ground radar measurements, with about 25\% more over ocean and 25\% less over
land and coastal areas. Retrieved rain rates from the improved (Version 6) 2A-12 algorithm will be compared to 2A-25, 2B-31,
and ground-based radar measurements to evaluate the impact of improvements to 2A-12 in Version 6. An important
improvement to the Version 6 2A-12 algorithm is the retrieval of Q1/Q2 (latent heating/drying) profiles in addition to the
surface rain rate and hydrometeor profiles. In order to ascertain the credibility of the new products, retrieved Q1/Q2 profiles
are compared to independent ground-based estimates. Analyses of dual-Doppler radar data in conjunction with coincident
rawinsonde data yield estimates of the vertical distributions of diabatic heating/drying at high horizontal resolution for selected
cases over the Kwajalein and LBA field sites. The estimated vertical heating/drying structures appear to be reasonable.
Comparisons of Q1/Q2 profiles from Version 6 2A-12 and the ground-based estimates are in progress. Retrieved Q1/Q2
structures will also be compared to MM5 hurricane simulations for selected cases. The results of these intercomparisons will
be presented at the conference.
Author
Algorithms; Meteorological Radar; Radar Measurement; Rain; Radar Data
20030025244 NASA Goddard Space Flight Center, Greenbelt, MD, USA
Observational Evidence that Soil Moisture Variations Affect Precipitation
Koster, Randal D.; Suarez, Max J.; Higgins, R. Wayne; VandenDool, Huug M.; [2002]; 14 pp.; In English; No Copyright;
Avail: CASI; A03, Hardcopy
Land-atmosphere feedback, by which precipitation-induced soil moisture anomalies affect subsequent precipitation, may
be an important element of Earth’s climate system, but its very existence has never been demonstrated conclusively at regional
to continental scales. Evidence for the feedback is sought in a 50-year observational precipitation dataset covering the USA.
The precipitation variance and autocorrelation fields are characterized by features that agree (in structure, though not in
magnitude) with those produced by an atmospheric general circulation model (AGCM). Because the model-generated features
are known to result from land-atmosphere feedback alone, the observed features are highly suggestive of the existence of
feedback in nature.
Author
Soil Moisture; Precipitation (Meteorology); Climatology; Atmospheric General Circulation Models; Air Land Interactions
20030025274 NASA Goddard Space Flight Center, Greenbelt, MD, USA
Overlap Properties of Clouds Generated by a Cloud Resolving Model
Oreopoulos, L.; Khairoutdinov, M.; December 2002; 22 pp.; In English; Original contains black and white illustrations
Contract(s)/Grant(s): NAG5-11631; DE-AI02-00ER-62939; No Copyright; Avail: CASI; A03, Hardcopy
In order for General Circulation Models (GCMs), one of our most important tools to predict future climate, to correctly
describe the propagation of solar and thermal radiation through the cloudy atmosphere a realistic description of the vertical
distribution of cloud amount is needed. Actually, one needs not only the cloud amounts at different levels of the atmosphere,
but also how these cloud amounts are related, in other words, how they overlap. Currently GCMs make some idealized
assumptions about cloud overlap, for example that contiguous cloud layers overlap maximally and non-contiguous cloud
layers overlap in a random fashion. Since there are difficulties in obtaining the vertical profile of cloud amount from
observations, the realism of the overlap assumptions made in GCMs has not been yet rigorously investigated. Recently
however, cloud observations from a relatively new type of ground radar have been used to examine the vertical distribution
of cloudiness. These observations suggest that the GCM overlap assumptions are dubious. Our study uses cloud fields from
sophisticated models dedicated to simulate cloud formation, maintenance, and dissipation called Cloud Resolving Models .
These models are generally considered capable of producing realistic three-dimensional representation of cloudiness. Using
numerous cloud fields produced by such a CRM we show that the degree of overlap between cloud layers is a function of their
separation distance, and is in general described by a combination of the maximum and random overlap assumption, with
random overlap dominating as separation distances increase. We show that it is possible to parameterize this behavior in a way
that can eventually be incorporated in GCMs. Our results seem to have a significant resemblance to the results from the radar
observations despite the completely different nature of the datasets. This consistency is encouraging and will promote
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